Elsevier

Bone

Volume 76, July 2015, Pages 1-4
Bone

Original Full Length Article
FRAX (Aus) and falls risk: Association in men and women

https://doi.org/10.1016/j.bone.2015.03.004Get rights and content

Highlights

  • Falls increase fracture risk, but are not currently included in the FRAX prediction tool.

  • We investigated the association of FRAX and falls risk.

  • Falls risk was weakly correlated with FRAX scores.

  • However, the relationship was non-significant after adjustment for age and sex.

  • Inclusion of falls in FRAX could be further explored.

Abstract

Purpose

The WHO fracture risk prediction tool (FRAX®) utilises clinical risk factors to estimate the probability of fracture over a 10-year period. Although falls increase fracture risk, they have not been incorporated into FRAX. It is currently unclear if FRAX captures falls risk and whether addition of falls would improve fracture prediction. We aimed to investigate the association of falls risk and Australian-specific FRAX.

Methods

Clinical risk factors were documented for 735 men and 602 women (age 40–90 yr) assessed at follow-up (2006–2010 and 2000–2003, respectively) of the Geelong Osteoporosis Study. FRAX scores with and without BMD were calculated. A falls risk score was determined at the time of BMD assessment and self-reported incident falls were documented from questionnaires returned one year later. Multivariable analyses were performed to determine: (i) cross-sectional association between FRAX scores and falls risk score (Elderly Falls Screening Test, EFST) and (ii) prospective relationship between FRAX and time to a fall.

Results

There was an association between FRAX (hip with BMD) and EFST scores (β = 0.07, p < 0.001). After adjustment for sex and age, the relationship became non-significant (β = 0.00, p = 0.79). The risk of incident falls increased with increasing FRAX (hip with BMD) score (unadjusted HR 1.04, 95% CI 1.02, 1.07). After adjustment for age and sex, the relationship became non-significant (1.01, 95% CI 0.97, 1.05).

Conclusions

There is a weak positive correlation between FRAX and falls risk score, that is likely explained by the inclusion of age and sex in the FRAX model. These data suggest that FRAX score may not be a robust surrogate for falls risk and that inclusion of falls in fracture risk assessment should be further explored.

Introduction

Fractures are a major health concern because of the public health costs associated with diagnosis (X-ray) and management that may include hospitalisation and rehabilitation [1]. Additionally, fracture may be associated with excess morbidity [2] and mortality [3] and increases the risk of subsequent fractures [4] that further increases the public health burden [1]. It is important to identify those at the highest risk of fracture to ensure that treatment is targeted to those who are most likely to benefit and to avoid expensive treatments in those at lower risk [5].

FRAX is a fracture risk assessment algorithm developed by the World Health Organization (WHO) and was designed for use in primary care to assess 10-year fracture probability in men and women aged 40 to 90 years who have not previously received pharmacological treatment to prevent fractures [5]. It combines clinical risk factors (age, body mass index, prior fracture, parental hip fracture, smoking, glucocorticoid use, rheumatoid arthritis, secondary osteoporosis and daily alcohol intake) to estimate the 10 year probability of hip and major osteoporotic fracture in men and women [6]. Bone mineral density (BMD) can be incorporated into the FRAX calculation to improve the predictions, but it is not necessary for fracture risk calculations [5]. Additionally, FRAX has multiple versions with different predictions for 53 countries, allowing more accurate fracture predictions based on data from the country of interest.

The FRAX tool is widely accepted for predicting fractures, but there have been suggestions as to how to improve it [5]. One important fracture risk factor that is not included is falls history; this was not included due to two reasons. First, uniform falls data were not available from all cohorts that contributed to FRAX and second, pharmaceutical intervention has not been shown to reduce fracture risk in those with a high falls risk [7]. For the latter, identification of those at high risk might only be useful if the risk can be reduced through some type of intervention. Falls strongly predict hip and other non-vertebral fracture independent of BMD and bone strength [5], [8], [9], and fallers are suggested to be at a higher fracture risk than FRAX may estimate [7]. Falls are common in the community-dwelling elderly occurring in 28–35% of 65 year olds and 42% of those over 75 years [10]. One method of measuring falls risk is the “Timed Up and Go Test” (TUG) which involves recording the time taken for an individual to rise from a chair, walk 3 m and return to sitting in the chair. The TUG test is mainly used for assessing the physical performance and functional mobility among elderly individuals, but it is also a good technique for determining individuals at a high risk of falls [11]. Falls risk assessment can be enhanced by incorporating more measurements into the assessment as in the Elderly Falls Screening Test (EFST) which includes prior falls, injurious falls, near falls, slow walking speed and gait [8].

There are recent data suggesting that the exclusion of falls risk from FRAX fracture risk assessments is not critical because the FRAX score is a surrogate for falls risk in elderly men [12]. Therefore, we hypothesised that falls risk is dependent on FRAX score. Thus, the aim of our study was to investigate the association of falls risk and Australian-specific FRAX 10-year probability scores from an Australian cohort study to determine if falls risk is predicted by FRAX score.

Section snippets

Participants

This current work incorporates data from the Geelong Osteoporosis Study (GOS) which includes residents form the Barwon Statistical Division (BSD), a region of south-eastern Australia [13]. This region has a number of factors that make it ideal for epidemiological studies including a large, stable population (n ~ 250,000) as well as multiple social, cultural and geographical settings. It is also representative of the Australian population. Further details of the study are provided elsewhere [13].

Descriptive characteristics

The descriptive characteristics of the participants are shown in Table 1. Those with a fall prior to baseline (5 year follow-up for men and 6 year follow-up for women) were older, lighter and shorter than those who did not fall prior to baseline. Prior fallers also had more prior fractures (45.3% vs 34.1%) and a higher proportion of rheumatoid arthritis (9.4% vs 3.9%). Individuals who fell during the 12 month follow-up were older, had a higher proportion of prior fractures (45.5% vs 35.1%) as well

Discussion

In this study, we report a clinically non-significant cross-sectional association between FRAX and EFST score. This relationship became statistically non-significant when adjusted for age and sex. Additionally, there was a weak association between FRAX and incident falls that became non-significant after adjusting for age and sex.

A meeting involving a joint Task Force including members from the International Osteoporosis Foundation (IOF) and the International Society for Clinical Densitometry

Conclusions

There is a weak positive correlation between FRAX and falls risk score, that is likely explained by the inclusion of age and sex in the FRAX model. These data suggest that FRAX score may not be a robust surrogate for falls risk and that inclusion of falls in fracture risk assessment should be further explored.

Acknowledgments

The study was funded by grants from the National Health and Medical Council (NHMRC 251638, 299831, 628582), The University of Melbourne Research Grant Scheme, American Society for Bone and Mineral Research (ASBMR), Perpetual Trustees, Amgen (Europe) GmBH and the Geelong Region Medical Research Foundation.

Authors' roles: Study design: KLH, MAK, and JAP. Study conduct: JAP. Data analysis: SEL. Data interpretation: KLH, MAK, SEL, SLB, and JAP. Drafting manuscript: KLH. Revising manuscript content:

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